As the demands for mobile data and its services increase dynamically and rapidly, expansions of mobile networks are unavoidable and require an intelligent planning to mechanism to optimize the utilization of expensive network resources while maximizing the revenue and meeting the expected quality of service for customers.
There are several mechanisms for expanding mobile networks in response to demand requirements and how those affect the quality of service offered by the mobile network operator (“MNO”). For example, additional micro-processors of channel elements (e.g. Code Division Multiple Access) can be deployed to cope with increasing demand for bands of frequencies. Deployed radio transmitters can be upgraded with higher power configurations to handle higher demands for power. Coding schemes that typically serve up to 15 customers per sector can accommodate more customers by increasing the number of frequency channels used. Signal coverage can also be changed by adjusting the tilting angle of an antenna. In addition, a new base station may need to be added so as to reduce the loads of neighbouring base stations, to accommodate the introduction of a new set of customers, or to deal with sudden high demands due to the occurrence of unexpected events.
When an MNO makes a decision on the need to expand a mobile network segment, it does not only consider the degradation of network service due to the overload from a high demand, but it also needs to take into consideration the importance of the network segment in terms of, for example, the overall benefit of the expansion, the number of high-paying customers (also called prestige customers) that use this network segment, and its customer churn rate.
Some of the mechanisms used for expansion (e.g. the construction of a new base station) require an extended time frame to complete the task. In such scenarios, it is possible that customers (in particular prestige customers) may decide to change service providers before the completion of the expansion due to frustration from receiving poor service quality.
As a result, prioritizing the expansion of network segments according to the Quality of Service being delivered as well as potential customer churn rate is very important. By giving higher priority to the network segments that serve significant numbers of prestige customers that are experiencing relatively poor service quality, an MNO may be able to prevent these prestige customers from churning, and in turn avoid or reduce the risk of revenue loss.
St-Hilaire, M.; Chamberland, Steven; Pierre, Samuel, “Global expansion model for mobile networks,” Communications Letters, IEEE, vol. 10, no. 6, pp. 453, 455, June 2006 describes a mathematical model aimed at solving an expansion problem of Universal Mobile Telecommunications System (UMTS) networks. UMTS is a 3G mobile cellular system based on the GSM network standard. The proposed model attempts to solve the mobile access network and the core network simultaneously by minimizing the network expansion cost. The cost includes adding or removing network nodes and links subject to the number of subscribers in that area, traffic flow conversation constraints and equipment and links constraints. A set of potential sites to install different types of nodes (e.g. cell sites, Radio Network Controllers (RNCs), Mobile Switching Centers (MSCs)) is first identified. Different types of network nodes and connections have different associated costs. A Mixed Integer Linear Programming (MILP) based approach is then applied to minimize the total cost. An expansion model for 3G mobile networks is proposed with small instances as pointed out by the authors. However, during the network expansion, no consideration is made of historical data, geographical locations and high value generated customers.
Szlovencsak, A.; Godor, I.; Harmatos, J.; Cinkler, T., “Planning reliable UMTS terrestrial access networks,” Communications Magazine, IEEE, vol. 40, no. 1, pp. 66, 72, January 2002 doi: 10.1109/35.978051 describes two different heuristic algorithms based on the Simulated Annealing approach to perform reliable planning for UMTS access networks. It assumes that locations of radio base stations (RBSs) and a single location of radio network controller (RNC) with the traffic demands and reliability expectations are given. The objective is to form a minimum cost tree network to connect a set of RBSs to a single RNC in order to fulfil the given topology constraints and traffic demands. In addition, reliability enhancement algorithms are introduced by inserting additional links to improve the network reliability for the single failure scenario. Thus this paper is mainly focused on the mobile access network planning with the consideration of protecting the most failure-sensitive parts of the topology.
U.S. Pat. No. 7,561,876 relates to mobile communication network planning and mobility management optimization. The system described in this patent generates results of mobility management optimization and the deployment plan which provides recommendations on how to restructure network elements to improve traffic and balance loads within a network planning area. Different algorithms such as K-L algorithm, Greedy algorithm, Genetic algorithm, and Simulated Annealing algorithm, etc. are provided to optimize the network plan. A graphical user interface (GUI) front-end is included in the planning system to allow users to analyse traffic conditions and determine an optimal plan which meets customer's requirements (e.g. reducing signalling congestion, improving inter-Base Station Controller (BSC)/Mobile Switching Center (MSC) handover failures, a higher quality of service, etc.). The inputs to the system are the network topology and the associated network statistic data which includes traffic behaviours of network elements in the mobile network. In addition, a mobility model is employed to characterize subscriber movement patterns at different times within the mobile network.
US 2010/0149984 describes a system which monitors call routing, IP traffic, statistics, signalling and CDRs (Customer Data Records). The system maintains an offline database of the network for a service provider; updating database during non-peak hours of operation and maintaining updated statistics generated by the network switches. Based on the analysis of information gathered, the system generates different scenarios to optimize the network and provide expansion plans. It is achieved by creating auto mappings and adding trunk automatically for traditional 2G switching environment. For 4G technologies or IP based network solutions, it re-routes the traffic/packets depending on the least cost routing. In addition, the system analyzes traffic, handovers, RF parameters processor load, utilization of the ports and suggest BSC/RNC/Routers re-routing of network elements for the expansion of BSS network. The system is also capable of providing a traffic model and capturing the case where a network element is added or deleted manually or automatically.
U.S. Pat. No. 8,385,900 describes a method to modify the communication parameters of a wireless fixed network to maximize the coverage. The method includes obtaining measurements of transmit power, frequency channel, modulation and coding scheme for at least two sectors of the wireless network when a signal strength indicator of one or more sectors is at or below a target value. A software program with the self-optimizing algorithm is executed to modify communication parameters such as base station transmitting power, antenna tilt angle, channel bandwidth and antenna gain. In addition, the coverage map is presented by the approximated Voronoi cells. A load balancing algorithm is then employed to move vertexes of the Voronoi diagrams to balance traffic for the congested regions. However, this patent only focuses on technical data to maximize the coverage.
An object of the present invention is to provide methods and systems for monitoring mobile networks which provide proactive monitoring and/or are predictive in nature.
A further object of the present invention is to provide network expansion systems and methods which are preferably fully automated, adaptive and require minimal or no human intervention.